comparison image_learner.xml @ 9:9e912fce264c draft

planemo upload for repository https://github.com/goeckslab/gleam.git commit eace0d7c2b2939029c052991d238a54947d2e191

8:85e6f4b2ad18

<tool id="image_learner" name="Image Learner for Classification" version="0.1.2" profile="22.05">
    <description>trains and evaluates a image classification model</description>
    <requirements>
        <container type="docker">quay.io/goeckslab/galaxy-ludwig-gpu:latest</container>
    </requirements>
    <required_files>
        <include path="utils.py" />

                    #end if
                    #if $batch_size_define == "true"
                        --batch-size "$batch_size"
                    #end if
                    --split-probabilities "$train_split" "$val_split" "$test_split"
                #end if
                #if $augmentation
                    --augmentation "$augmentation"
                #end if
                --random-seed "$random_seed"

        </param>

        <conditional name="scratch_fine_tune">
            <param name="use_pretrained" type="select"
                label="Use pretrained weights?"
                help="If select no, the encoder, combiner, and decoder will all be initialized and trained from scratch.  
               (e.g. when your images are very different from ImageNet or no suitable pretrained model exists.)">
                <option value="false">No</option>
                <option value="true" selected="true">Yes</option>
            </param>
            <when value="true">
                <param name="fine_tune" type="select" label="Fine tune the encoder?"

                        <has_n_columns n="1" />
                    </assert_contents>
                </element>
            </output_collection>
        </test>
        </tests>
    <help>
        <![CDATA[
**What it does**
Image Learner for Classification: trains and evaluates a image classification model. 
It uses the metadata csv to find the image paths and labels. 
The metadata csv should contain a column with the name 'image_path' and a column with the name 'label'.
Optionally, you can also add a column with the name 'split' to specify which split each row belongs to (train, val, test). 
If you do not provide a split column, the tool will automatically split the data into train, val, and test sets based on the proportions you specify or [0.7, 0.1, 0.2] by default.


**Outputs**
The tool will output a trained model in the form of a ludwig_model file,
a report in the form of an HTML file, and a collection of CSV/json/png files containing the predictions, experiment stats and visualizations.
The html report will contain metrics&experiment setup parameters, train&val plots and test plots.

9:9e912fce264c

<tool id="image_learner" name="Image Learner" version="0.1.2" profile="22.05">
    <description>trains and evaluates an image classification/regression model</description>
    <requirements>
        <container type="docker">quay.io/goeckslab/galaxy-ludwig-gpu:latest</container>
    </requirements>
    <required_files>
        <include path="utils.py" />

                    #end if
                    #if $batch_size_define == "true"
                        --batch-size "$batch_size"
                    #end if
                    --split-probabilities "$train_split" "$val_split" "$test_split"
                    #if $threshold
                        --threshold "$threshold"
                    #end if
                #end if
                #if $augmentation
                    --augmentation "$augmentation"
                #end if
                --random-seed "$random_seed"

        </param>

        <conditional name="scratch_fine_tune">
            <param name="use_pretrained" type="select"
                label="Use pretrained weights?"
                help="If select no, the encoder, combiner, and decoder will all be initialized and trained from scratch. (e.g. when your images are very different from ImageNet or no suitable pretrained model exists.)">
                <option value="false">No</option>
                <option value="true" selected="true">Yes</option>
            </param>
            <when value="true">
                <param name="fine_tune" type="select" label="Fine tune the encoder?"

                        <has_n_columns n="1" />
                    </assert_contents>
                </element>
            </output_collection>
        </test>
    </tests>
    <help>
        <![CDATA[
**What it does**
Image Learner for Classification/regression: trains and evaluates a image classification/regression model.
It uses the metadata csv to find the image paths and labels. 
The metadata csv should contain a column with the name 'image_path' and a column with the name 'label'.
Optionally, you can also add a column with the name 'split' to specify which split each row belongs to (train, val, test). 
If you do not provide a split column, the tool will automatically split the data into train, val, and test sets based on the proportions you specify or [0.7, 0.1, 0.2] by default.

**If the selected label column has more than 10 unique values, the tool will automatically treat the task as a regression problem and apply appropriate metrics (e.g., MSE, RMSE, R²).**

**Outputs**
The tool will output a trained model in the form of a ludwig_model file,
a report in the form of an HTML file, and a collection of CSV/json/png files containing the predictions, experiment stats and visualizations.
The html report will contain metrics&experiment setup parameters, train&val plots and test plots.